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videos_zip_two_refiners/logs/refiner/LongLive-2.0_pass1_x2.log

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+Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
+
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+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/video_refiner_upscale.py", line 218, in <module>
+    main()
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/video_refiner_upscale.py", line 203, in main
+    input_shape, output_shape = pipeline(
+                                ^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/video_refiner_upscale.py", line 127, in __call__
+    video_state, _ = self.stage(
+                     ^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/blocks.py", line 432, in __call__
+    return self.run(
+           ^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/blocks.py", line 373, in run
+    video_state, audio_state = loop(
+                               ^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/samplers.py", line 69, in euler_denoising_loop
+    video_result, audio_result = denoiser(transformer, video_state, audio_state, sigmas, step_idx)
+                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/denoisers.py", line 215, in __call__
+    denoised_video, denoised_audio = transformer(video=pos_video, audio=pos_audio, perturbations=None)
+                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/batch_split.py", line 73, in forward
+    return self._model(video=video, audio=audio, perturbations=perturbations)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/block_streaming/wrapper.py", line 89, in forward
+    return self._model(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/model.py", line 483, in forward
+    vx, ax = self.velocity_model(video, audio, perturbations)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/model.py", line 402, in forward
+    video_args = self.video_args_preprocessor.prepare(video, audio) if video is not None else None
+                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/transformer_args.py", line 237, in prepare
+    transformer_args = self.simple_preprocessor.prepare(modality)
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/transformer_args.py", line 156, in prepare
+    timestep, embedded_timestep = self._prepare_timestep(
+                                  ^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/transformer_args.py", line 67, in _prepare_timestep
+    timestep, embedded_timestep = adaln(
+                                  ^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/adaln.py", line 45, in forward
+    return self.linear(self.silu(embedded_timestep)), embedded_timestep
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/linear.py", line 125, in forward
+    return F.linear(input, self.weight, self.bias)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 83.14 GiB. GPU 0 has a total capacity of 79.33 GiB of which 47.61 GiB is free. Including non-PyTorch memory, this process has 31.64 GiB memory in use. Of the allocated memory 30.95 GiB is allocated by PyTorch, and 177.07 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

videos_zip_two_refiners/logs/refiner/bf16_african_savanna_6_pass1_x2.log

@@ -0,0 +1,81 @@
+Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
+Number of frames 1530 of input video is not (8 * k + 1), last 1 frames will be cropped
+
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+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/video_refiner_upscale.py", line 218, in <module>
+    main()
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/video_refiner_upscale.py", line 203, in main
+    input_shape, output_shape = pipeline(
+                                ^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/video_refiner_upscale.py", line 127, in __call__
+    video_state, _ = self.stage(
+                     ^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/blocks.py", line 432, in __call__
+    return self.run(
+           ^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/blocks.py", line 373, in run
+    video_state, audio_state = loop(
+                               ^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/samplers.py", line 69, in euler_denoising_loop
+    video_result, audio_result = denoiser(transformer, video_state, audio_state, sigmas, step_idx)
+                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/denoisers.py", line 215, in __call__
+    denoised_video, denoised_audio = transformer(video=pos_video, audio=pos_audio, perturbations=None)
+                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/batch_split.py", line 73, in forward
+    return self._model(video=video, audio=audio, perturbations=perturbations)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/block_streaming/wrapper.py", line 89, in forward
+    return self._model(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/model.py", line 483, in forward
+    vx, ax = self.velocity_model(video, audio, perturbations)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/model.py", line 402, in forward
+    video_args = self.video_args_preprocessor.prepare(video, audio) if video is not None else None
+                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/transformer_args.py", line 237, in prepare
+    transformer_args = self.simple_preprocessor.prepare(modality)
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/transformer_args.py", line 166, in prepare
+    pe = self._prepare_positional_embeddings(
+         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/transformer_args.py", line 136, in _prepare_positional_embeddings
+    pe = precompute_freqs_cis(
+         ^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/rope.py", line 205, in precompute_freqs_cis
+    cos_freq, sin_freq = split_freqs_cis(freqs, pad_size, num_attention_heads)
+                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/rope.py", line 158, in split_freqs_cis
+    cos_freq = torch.concatenate([cos_padding, cos_freq], axis=-1)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 5.16 GiB. GPU 0 has a total capacity of 79.33 GiB of which 3.62 GiB is free. Including non-PyTorch memory, this process has 75.62 GiB memory in use. Of the allocated memory 74.98 GiB is allocated by PyTorch, and 127.68 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

videos_zip_two_refiners/logs/refiner/bf16_aquamarine_underwater_6_pass1_x2.log

@@ -0,0 +1,81 @@
+Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
+Number of frames 1530 of input video is not (8 * k + 1), last 1 frames will be cropped
+
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+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/video_refiner_upscale.py", line 218, in <module>
+    main()
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/video_refiner_upscale.py", line 203, in main
+    input_shape, output_shape = pipeline(
+                                ^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/video_refiner_upscale.py", line 127, in __call__
+    video_state, _ = self.stage(
+                     ^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/blocks.py", line 432, in __call__
+    return self.run(
+           ^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/blocks.py", line 373, in run
+    video_state, audio_state = loop(
+                               ^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/samplers.py", line 69, in euler_denoising_loop
+    video_result, audio_result = denoiser(transformer, video_state, audio_state, sigmas, step_idx)
+                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/denoisers.py", line 215, in __call__
+    denoised_video, denoised_audio = transformer(video=pos_video, audio=pos_audio, perturbations=None)
+                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/batch_split.py", line 73, in forward
+    return self._model(video=video, audio=audio, perturbations=perturbations)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/block_streaming/wrapper.py", line 89, in forward
+    return self._model(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/model.py", line 483, in forward
+    vx, ax = self.velocity_model(video, audio, perturbations)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/model.py", line 402, in forward
+    video_args = self.video_args_preprocessor.prepare(video, audio) if video is not None else None
+                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/transformer_args.py", line 237, in prepare
+    transformer_args = self.simple_preprocessor.prepare(modality)
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/transformer_args.py", line 166, in prepare
+    pe = self._prepare_positional_embeddings(
+         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/transformer_args.py", line 136, in _prepare_positional_embeddings
+    pe = precompute_freqs_cis(
+         ^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/rope.py", line 205, in precompute_freqs_cis
+    cos_freq, sin_freq = split_freqs_cis(freqs, pad_size, num_attention_heads)
+                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/rope.py", line 158, in split_freqs_cis
+    cos_freq = torch.concatenate([cos_padding, cos_freq], axis=-1)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 5.16 GiB. GPU 0 has a total capacity of 79.33 GiB of which 3.62 GiB is free. Including non-PyTorch memory, this process has 75.62 GiB memory in use. Of the allocated memory 74.98 GiB is allocated by PyTorch, and 127.68 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

videos_zip_two_refiners/logs/refiner/bf16_brown_bear_river_6_pass1_x2.log

@@ -0,0 +1,81 @@
+Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
+Number of frames 1530 of input video is not (8 * k + 1), last 1 frames will be cropped
+
  0%|          | 0/3 [00:00<?, ?it/s]
  0%|          | 0/3 [00:05<?, ?it/s]
+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/video_refiner_upscale.py", line 218, in <module>
+    main()
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/video_refiner_upscale.py", line 203, in main
+    input_shape, output_shape = pipeline(
+                                ^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/video_refiner_upscale.py", line 127, in __call__
+    video_state, _ = self.stage(
+                     ^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/blocks.py", line 432, in __call__
+    return self.run(
+           ^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/blocks.py", line 373, in run
+    video_state, audio_state = loop(
+                               ^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/samplers.py", line 69, in euler_denoising_loop
+    video_result, audio_result = denoiser(transformer, video_state, audio_state, sigmas, step_idx)
+                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-pipelines/src/ltx_pipelines/utils/denoisers.py", line 215, in __call__
+    denoised_video, denoised_audio = transformer(video=pos_video, audio=pos_audio, perturbations=None)
+                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/batch_split.py", line 73, in forward
+    return self._model(video=video, audio=audio, perturbations=perturbations)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/block_streaming/wrapper.py", line 89, in forward
+    return self._model(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/model.py", line 483, in forward
+    vx, ax = self.velocity_model(video, audio, perturbations)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fsw/portfolios/nvr/users/shyang/miniconda3/envs/ltx2_repro/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/model.py", line 402, in forward
+    video_args = self.video_args_preprocessor.prepare(video, audio) if video is not None else None
+                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/transformer_args.py", line 237, in prepare
+    transformer_args = self.simple_preprocessor.prepare(modality)
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/transformer_args.py", line 166, in prepare
+    pe = self._prepare_positional_embeddings(
+         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/transformer_args.py", line 136, in _prepare_positional_embeddings
+    pe = precompute_freqs_cis(
+         ^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/rope.py", line 205, in precompute_freqs_cis
+    cos_freq, sin_freq = split_freqs_cis(freqs, pad_size, num_attention_heads)
+                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/lustre/fs11/portfolios/nvr/projects/nvr_elm_llm/users/shyang/ltx2/packages/ltx-core/src/ltx_core/model/transformer/rope.py", line 158, in split_freqs_cis
+    cos_freq = torch.concatenate([cos_padding, cos_freq], axis=-1)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 5.16 GiB. GPU 0 has a total capacity of 79.33 GiB of which 3.62 GiB is free. Including non-PyTorch memory, this process has 75.62 GiB memory in use. Of the allocated memory 74.98 GiB is allocated by PyTorch, and 127.68 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

videos_zip_two_refiners/logs/refiner/bf16_cozy_red_room_6_pass1_x2.log

@@ -0,0 +1 @@
+Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.

videos_zip_two_refiners/logs/refiner/bf16_frying_egg_closeup_5_pass1_x2.log

@@ -0,0 +1 @@
+Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.

videos_zip_two_refiners/logs/refiner/bf16_indoor_grooming_scene_7_pass1_x2.log

@@ -0,0 +1 @@
+Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.

videos_zip_two_refiners/logs/refiner/bf16_indoor_tender_moment_0_pass1_x2.log

@@ -0,0 +1 @@
+Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.

videos_zip_two_refiners/logs/refiner/robot_demo_pass1_x2.log

@@ -0,0 +1,4 @@
+Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
+
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+Refined 640x352/569f -> 1280x704/569f

videos_zip_two_refiners/logs/refiner/robot_demo_pass2_x4.log

@@ -0,0 +1,4 @@
+Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
+
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+
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+Refined 1280x704/569f -> 2560x1408/569f